论文标题

使用基于代理的性网络模型来分析缓解措施控制衣原体的影响

Using an agent-based sexual-network model to analyze the impact of mitigation efforts for controlling chlamydia

论文作者

Azizi, Asma, Dewar, Jeremy, Qu, Zhuolin, Mac Hymanm, James

论文摘要

沙眼衣原体(CT)是美国据报最多的性传播感染,其主要原因是女性不孕和骨盆炎性疾病的主要原因。尽管数十年来筛查女性CT,但非裔美国人年轻人(AA)的比率提高。我们创建和分析基于代理的网络模型以了解CT的传播。我们对模型参数进行校准,以同意调查数据,显示CT患病率为12%的女性和10%的男性在路易斯安那州新奥尔良的AA中的患病率为10%。我们的模型是长期和偶然的合作伙伴关系。该网络通过保留数据中观察到的联合度分布来捕获个体的分类混合。我们比较了随机筛选男性的干预策略的效率,伴侣通知,其中包括伴侣治疗,伴侣筛查和重新筛选感染。我们比较在有没有测试的情况下对待感染者的伴侣之间的区别。我们观察到,尽管CT筛查,重新纠正和治疗大多数感染者的伴侣将降低患病率,但仅这些减轻就不足以控制流行病。当前的做法是对受感染者的伴侣进行治疗,而无需先测试感染。该模型预测,如果对所有受感染者的伴侣进行了足够数量的测试和治疗,那么可以减轻流行病的阈值条件。该阈值是由于治疗个人感染伴侣的伴侣创建的扩展治疗网络而产生的。尽管这些结论可以帮助设计未来的CT缓解研究,但我们警告读者,这些结论是用于数学模型,而不是现实世界,并且取决于模型假设的有效性。

Chlamydia trachomatis (Ct) is the most reported sexually transmitted infection in the United States with a major cause of infertility and pelvic inflammatory disease among women. Despite decades of screening women for Ct, rates increase among young African Americans (AA). We create and analyze an agent-based network model to understand the spread of Ct. We calibrate the model parameters to agree with survey data showing Ct prevalence of 12% of the women and 10% of the men in the 15-25 year-old AA in New Orleans, Louisiana. Our model accounts for long-term and casual partnerships. The network captures assortative mixing of individuals by preserving the joint-degree distributions observed in the data. We compare the efficiency of intervention strategies of randomly screening men, partner notification, which includes partner treatment, partner screening, and rescreening for infection. We compare the difference between treating partners of an infected person both with and without testing them. We observe that although increased Ct screening, rescreening and treating most of the partners of infected people will reduce the prevalence, these mitigations alone are not sufficient to control the epidemic. The current practice is to treat the partners of an infected individual, without first testing them for infection. The model predicts that if a sufficient number of the partners of all infected people are tested and treated, then there is a threshold condition where the epidemic can be mitigated. This threshold results from the expanded treatment network created by treating the partners of the infected partners of an individual. Although these conclusions can help design future Ct mitigation studies, we caution the reader that these conclusions are for the mathematical model, not the real world, and are contingent on the validity of the model assumptions.

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